Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Journal: Cartilage
Published Date: Sep 8, 2021

Abstract

OBJECTIVE: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

Authors

  • Kevin A Thomas
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Dominik Krzemiński
    Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK.
  • Łukasz Kidziński
    Stanford University Department of Bioengineering, Stanford, CA, United States of America.
  • Rohan Paul
    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Elka B Rubin
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Eni Halilaj
    Department of Mechanical Engineering, Carnegie Mellon University, United States. Electronic address: ehalilaj@andrew.cmu.edu.
  • Marianne S Black
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Akshay Chaudhari
    Stanford University, Stanford, CA, USA.
  • Garry E Gold
    Department of Radiology, Stanford University, Stanford, California.
  • Scott L Delp

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